Organizational knowledge includes symbols, routines, and resources that are used by an organization's members to coordinate action and interaction. The management of an organization's knowledge is of great strategic importance to that organization.
In order to study organizational knowledge, data must be obtained from organizational discourse, namely written texts and conversation. However, once organizational discourse is obtained, the massive amount of data resulting from such discourse must be analyzed.
Informational technology plays a key role in facilitating organizational knowledge and learning via a process of knowledge mapping, mining, and management. Knowledge mining involves the capture of organizational knowledge embedded in text and conversation; knowledge mapping involves representing such knowledge artifacts and their source and content in useful ways; and knowledge management involves the application of such analyses for organizational benefit. Text analysis comprises this process of mapping, mining, and managing.
There are many different approaches to text analysis. Three main approaches of text analysis are based on inference, positioning, and representation. Approaches based on inference draw conclusion about what is not given in the text. Inference approaches apply rules or learned patterns to content that is directly given in the text or, alternatively, distinguish important material from unimportant material using similar sets of rules. In positioning approaches, abstract profiles of texts are generated and then positioned using spatial modeling techniques that are relative to other texts in a set or corpus. Representational approaches produce representations of texts by extracting or distilling its given content without reference to a training set, corpus, semantic rule set, or field of other documents. The representations are instrinsically meaningful and do not depend on outside contexts or sources of information. Representational approaches include keyword indexes and network text analysis.
Those approaches based on a concept network are best suited for capturing discourse by computer. The common theme in network text analysis is that text can be represented as a network of co-occurrences of words. The variations are in the details of how the links are formed. In most existing approaches, words are counted as being linked if they co-occur within some arbitrarily sized “window” of words as it is slid along the text. Once the text is represented in the form of a network of concepts, it is susceptible to a range of powerful analysis techniques that can describe the structural properties of particular words and/or the overall network.
However, in network text analysis, there is not much consistency in the ways that researchers represent text as a network. Problems include 1) that the criteria for unitizing the text are seldom well established, and 2) that the networks themselves are not very thoroughly conceptualized (i.e. researchers do not always address what a link means in theoretical terms or state exactly what it is that flows through a network of words).
Accordingly, there is a need for a representational network text analysis approach which 1) is based on a network representation of associated words that takes advantage of the complex data structure offered by that network, 2) represents intentional, discursive acts of competent authors and speakers, and 3) is versatile and transportable across contexts.